The Bueda Semantic Analysis Engine claims to be able to translate user tags into categories, ontologies and structured content definitions. The system is based on research carried out by Carnegie Mellon University’s Language Technologies Institute.
Creating meaningful semantic data from unstructured sources (like user tags) is one of the fundamental problems for content repositories of all kinds – including Digital Asset Management systems so we were very interested to try it out. As with many solutions of this kind, as decision support (i.e. suggesting clean tags) they could be very useful and save a lot of time and improve cataloguing consistency, but they are still possibly a bit risky if fully automated.
When we tested the web based demo, the results were a bit hit and miss – but not at all bad and would probably be acceptable in a very large repository where a certain level of inaccuracy is more likely to be tolerated (certainly as compared with doing the work manually).
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